A method of imaging includes obtaining a plurality of dynamic sinograms, each dynamic sinogram representing detection events of gamma rays at a plurality of detector elements, summing the plurality of dynamic sinograms to generate an activity map based on a radioactivity level of the gamma rays; reconstructing, using the plurality of dynamic sinograms, a plurality of dynamic images, each of the plurality of dynamic images corresponding to one of the each of the plurality of dynamic sinograms, and generating, using the plurality of dynamic sinograms and the activity map, at least one parametric image.
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1. An apparatus, comprising: processing circuitry configured to obtain a plurality of dynamic sinograms, each of the plurality of dynamic sinograms representing detection events of gamma rays at a plurality of detector elements, sum the plurality of dynamic sinograms to generate an activity map based on a radioactivity level of the gamma rays, reconstruct, using the plurality of dynamic sinograms, a plurality of dynamic images, each of the plurality of dynamic images corresponding to one of the each of the plurality of dynamic sinograms, and generate, using the plurality of dynamic sinograms and the activity map, at least one parametric image.
This invention relates to medical imaging systems, specifically positron emission tomography (PET) scanners, which capture dynamic data to analyze physiological processes. The apparatus includes processing circuitry that obtains multiple dynamic sinograms, each representing gamma-ray detection events from a PET scan at different time points. These sinograms are summed to create an activity map showing radioactivity distribution. The circuitry also reconstructs dynamic images from the sinograms, each image corresponding to a specific time point. Additionally, the system generates parametric images by combining the dynamic sinograms and the activity map, providing quantitative metrics such as blood flow or metabolic rates. The parametric images enhance diagnostic accuracy by highlighting temporal changes in tracer uptake. This approach improves upon traditional static PET imaging by capturing and analyzing temporal variations in radioactivity, enabling better assessment of dynamic biological processes. The system integrates data processing, image reconstruction, and parametric mapping to provide comprehensive functional insights from PET scans.
2. The apparatus of claim 1 , wherein the circuitry is further configured to generate a time activity curve for the each of the plurality of dynamic images, and the at least one parametric image is generated using the activity map and the time activity curve for the each of the plurality of dynamic images.
This invention relates to medical imaging systems, specifically apparatuses for generating parametric images from dynamic imaging data. The problem addressed is the need for improved analysis of time-varying physiological data, such as in nuclear medicine or dynamic contrast-enhanced imaging, where capturing temporal changes in activity or contrast distribution is critical for diagnosis. The apparatus includes circuitry configured to process a sequence of dynamic images acquired over time, such as from PET, SPECT, or CT scans. The circuitry generates an activity map representing spatial distribution of activity or contrast in the imaged subject. Additionally, it produces a time activity curve for each of the dynamic images, which quantifies how activity or contrast changes over time at each spatial location. The parametric image is then derived by combining the activity map with the time activity curves, providing a single image that encodes both spatial and temporal information. This allows for enhanced visualization and quantification of physiological processes, such as blood flow, metabolic activity, or contrast agent distribution. The invention improves upon prior methods by integrating temporal data into parametric imaging, enabling more accurate assessment of dynamic biological processes. The apparatus may be used in clinical settings to improve diagnostic accuracy in conditions where temporal changes are clinically significant, such as tumor characterization, cardiac perfusion, or neurological disorders. The parametric images generated can be used for quantitative analysis, aiding in treatment planning and monitoring.
3. The apparatus of claim 2 , wherein the circuitry is further configured to generate the at least one parametric image using an objective function including a penalty term, wherein the penalty term includes one or more spatially-varying smoothing parameters determined from the activity map.
This invention relates to medical imaging systems, specifically apparatuses for generating parametric images from medical imaging data. The problem addressed is improving the accuracy and quality of parametric images, which are used to quantify physiological or functional properties in medical imaging, by incorporating spatially-varying smoothing parameters derived from an activity map. The apparatus includes circuitry configured to process imaging data to generate at least one parametric image. The circuitry is further configured to use an objective function that includes a penalty term to enhance the parametric image. The penalty term incorporates one or more spatially-varying smoothing parameters, which are determined from an activity map derived from the imaging data. The activity map identifies regions of high and low activity, allowing the smoothing parameters to adapt dynamically to the underlying data. This adaptive smoothing helps reduce noise and artifacts in regions of low activity while preserving fine details in regions of high activity, resulting in a more accurate and interpretable parametric image. The apparatus may also include additional circuitry for generating the activity map, which can be derived from the imaging data itself or from a separate imaging modality. The spatially-varying smoothing parameters are calculated based on the activity map to ensure optimal smoothing across different regions of the image. This approach improves the reliability of parametric imaging in applications such as positron emission tomography (PET), single-photon emission computed tomography (SPECT), or other functional imaging techniques.
4. The apparatus of claim 3 , wherein the penalty term includes a first parameter controlling the degree of penalty based on a Patlak slope of the time activity curve, and a second parameter controlling the degree of penalty based on a Patlak intercept of the time activity curve.
This invention relates to medical imaging and quantitative analysis of time-activity curves (TACs) in positron emission tomography (PET) or other imaging modalities. The problem addressed is improving the accuracy of kinetic modeling by penalizing unrealistic TAC features, such as those that deviate from expected physiological behavior. The apparatus includes a processing system configured to analyze TACs derived from imaging data. A penalty term is applied during optimization to enforce physiological plausibility. The penalty term has two adjustable parameters: one controls the penalty based on the Patlak slope (indicating irreversible tracer uptake), and the other controls the penalty based on the Patlak intercept (indicating initial tracer distribution). These parameters allow fine-tuning of the model to match expected biological behavior, reducing errors in kinetic parameter estimation. The apparatus may also include a display for visualizing the optimized TACs and parameters, and an input interface for adjusting the penalty parameters. The system can be used in clinical or research settings to improve the reliability of quantitative PET analysis, particularly in studies of metabolism or receptor binding. The penalty term helps avoid overfitting and ensures that derived parameters remain within biologically plausible ranges.
5. The apparatus of claim 3 , wherein the circuitry is further configured to segment the activity map based on types of organs represented therein, and generate the at least one parametric image, wherein the penalty term is based on the segmentation of the activity map.
This invention relates to medical imaging systems, specifically for improving the accuracy of parametric imaging in nuclear medicine or positron emission tomography (PET) scans. The technology addresses the challenge of accurately quantifying physiological parameters, such as metabolic activity, in the presence of noise and anatomical variability. Traditional methods often produce blurred or inaccurate parametric images due to insufficient modeling of organ-specific activity distributions. The apparatus includes circuitry configured to generate an activity map from imaging data, such as PET scans, and then segment this map into distinct regions corresponding to different organs. The segmentation helps isolate organ-specific activity patterns, which are then used to generate parametric images. A penalty term, derived from the segmentation, is applied during image reconstruction to enforce anatomical consistency and reduce artifacts. This ensures that the parametric images accurately reflect the physiological activity within each organ, improving diagnostic reliability. The segmentation step involves classifying regions of the activity map based on organ types, which may be identified using anatomical atlases or machine learning models. The penalty term adjusts the reconstruction process to prioritize anatomical coherence, minimizing distortions caused by noise or partial volume effects. This approach enhances the precision of quantitative imaging, particularly in applications like tumor detection, metabolic mapping, or treatment monitoring. The system may integrate with existing PET or SPECT scanners, providing a software-based solution for improved parametric image generation.
6. The apparatus of claim 2 , wherein the circuitry is further configured to generate the at least one parametric image using an objective function including a penalty term, wherein the penalty term includes an edge-preserving potential function that has a shape based on the activity map.
This invention relates to medical imaging systems, specifically apparatuses for generating parametric images from medical imaging data. The problem addressed is the challenge of accurately reconstructing parametric images while preserving important anatomical features, such as edges, which are often blurred or lost in conventional reconstruction methods. The apparatus includes circuitry configured to process medical imaging data, such as from PET, SPECT, or CT scans, to generate parametric images. These images represent quantitative parameters, such as blood flow or metabolic rates, derived from the raw imaging data. The circuitry is further configured to apply an objective function during reconstruction, which includes a penalty term designed to enhance image quality. The penalty term incorporates an edge-preserving potential function that adapts its shape based on an activity map derived from the imaging data. The activity map highlights regions of high signal intensity, which are typically associated with anatomical edges or boundaries. By dynamically adjusting the penalty term's shape according to the activity map, the apparatus ensures that edges are preserved while suppressing noise and artifacts in homogeneous regions. This approach improves the accuracy and interpretability of parametric images, aiding in clinical diagnosis and treatment planning. The invention is particularly useful in applications where precise quantification of physiological parameters is critical, such as oncology, neurology, and cardiology.
7. The apparatus of claim 1 , wherein the circuitry is further configured to generate the at least one parametric image using a voxel-by-voxel parametric fitting for the each of the plurality of dynamic sinograms.
This invention relates to medical imaging, specifically to apparatuses for generating parametric images from dynamic sinograms in positron emission tomography (PET) or similar imaging modalities. The problem addressed is the need for accurate and efficient quantification of physiological parameters, such as blood flow or metabolic rates, from dynamic imaging data. The apparatus includes circuitry configured to process dynamic sinograms, which are time-resolved projection data acquired during a PET scan. The circuitry performs a voxel-by-voxel parametric fitting for each of the dynamic sinograms to generate at least one parametric image. This fitting process involves applying a mathematical model to each voxel in the sinograms to estimate parameters of interest, such as kinetic rates or tracer distribution volumes. The parametric images provide spatially resolved maps of these parameters, enabling quantitative analysis of physiological processes. The circuitry may also reconstruct the dynamic sinograms into a series of time-resolved images before fitting, ensuring accurate parameter estimation. The parametric fitting can be performed using linear or nonlinear regression techniques, tailored to the specific tracer and physiological model being used. The resulting parametric images enhance diagnostic capabilities by providing quantitative insights into tissue function beyond traditional anatomical imaging. This approach improves the clinical utility of PET by enabling precise measurement of biological processes in vivo.
8. The apparatus of claim 1 , further comprising: a detector including the plurality of detector elements configured to detect the gamma rays generated from a tracer.
A medical imaging apparatus is designed to detect gamma rays emitted by a radioactive tracer within a patient's body. The apparatus includes a detector with multiple detector elements that capture the gamma rays, enabling the creation of diagnostic images. The detector elements are arranged to efficiently collect radiation data, which is then processed to form high-resolution images of internal structures. This technology is used in nuclear medicine for applications such as positron emission tomography (PET) or single-photon emission computed tomography (SPECT), where precise detection of gamma rays is critical for accurate diagnosis. The apparatus improves imaging accuracy by enhancing the sensitivity and spatial resolution of the detector elements, allowing for better visualization of tracer distribution within the body. The system may also include additional components, such as collimators or signal processing units, to further refine the imaging process. The invention addresses the need for more precise and efficient gamma-ray detection in medical imaging, improving diagnostic capabilities in nuclear medicine.
9. The apparatus of claim 1 , wherein the activity map is generated using an ordered-subset expectation maximization (OSEM) method, which is iteratively repeated until a predetermined stopping criteria is reached.
This invention relates to medical imaging systems, specifically apparatuses for generating activity maps in nuclear medicine imaging, such as positron emission tomography (PET) or single-photon emission computed tomography (SPECT). The problem addressed is the need for accurate and efficient reconstruction of activity distributions within a patient's body from detected radiation events, which is computationally intensive and prone to noise. The apparatus includes a radiation detector array configured to capture radiation events emitted from a radiopharmaceutical within a patient. The system processes these events to generate an activity map representing the spatial distribution of the radiopharmaceutical. The key improvement involves using an ordered-subset expectation maximization (OSEM) method for reconstruction. OSEM is an iterative algorithm that divides the projection data into subsets, updating the image estimate in multiple passes to improve convergence speed and reduce noise compared to traditional full-data methods. The iteration process continues until a predetermined stopping criterion is met, such as a fixed number of iterations or a convergence threshold, ensuring a balance between computational efficiency and image quality. This approach enhances image reconstruction by accelerating convergence and improving signal-to-noise ratio, making it suitable for real-time or near-real-time clinical applications. The method is particularly useful in dynamic imaging, where rapid updates of activity maps are required.
10. The apparatus of claim 1 , wherein the dynamic sinograms are obtained over a predetermined duration of time at predetermined intervals.
A medical imaging system captures dynamic sinograms, which are time-resolved projection data sets representing internal structures of a subject. The system addresses the challenge of obtaining high-quality, time-resolved imaging data for dynamic processes, such as blood flow or organ movement, without excessive radiation exposure or prolonged scanning times. The apparatus includes a radiation source and a detector array configured to acquire sinograms at predetermined intervals over a specified duration. These intervals and duration are selected based on the temporal resolution required for the specific imaging application. The dynamic sinograms are then processed to reconstruct time-resolved images, enabling visualization of changes in the subject's internal structures over time. The system may also include a control unit to adjust acquisition parameters, such as radiation dose or detector sensitivity, to optimize image quality while minimizing exposure. This approach allows for real-time or near-real-time monitoring of dynamic physiological processes, improving diagnostic accuracy in applications like cardiac imaging, perfusion studies, or tumor tracking. The apparatus may further incorporate motion correction algorithms to compensate for subject movement during data acquisition, ensuring consistent image quality throughout the scanning period.
11. A method of imaging, comprising: obtaining a plurality of dynamic sinograms, each dynamic sinogram representing detection events of gamma rays at a plurality of detector elements; summing the plurality of dynamic sinograms to generate an activity map based on a radioactivity level of the gamma rays; reconstructing, using the plurality of dynamic sinograms, a plurality of dynamic images, each of the plurality of dynamic images corresponding to one of the each of the plurality of dynamic sinograms; and generating, using the plurality of dynamic sinograms and the activity map, at least one parametric image.
This invention relates to dynamic imaging techniques for gamma-ray detection, particularly in medical imaging applications such as positron emission tomography (PET). The method addresses the challenge of capturing and analyzing time-varying radioactive decay events to produce detailed functional and anatomical information. The process begins by acquiring multiple dynamic sinograms, where each sinogram records gamma-ray detection events across an array of detector elements over a specific time interval. These sinograms are then combined to create an activity map, which visually represents the spatial distribution of radioactivity based on the detected gamma-ray levels. Additionally, the individual dynamic sinograms are used to reconstruct a series of dynamic images, each corresponding to a specific time interval and capturing the temporal changes in radioactive decay. The method further integrates the dynamic sinograms and the activity map to generate parametric images, which provide quantitative metrics such as blood flow, metabolic rates, or other physiological parameters. This approach enhances the diagnostic capabilities of gamma-ray imaging by enabling both spatial and temporal analysis of radioactive tracer distributions within a subject.
12. The method of claim 11 , further comprising: generating a time activity curve for the each of the plurality of dynamic images, wherein the at least one parametric image is generated using the activity map and the time activity curve for the each of the plurality of dynamic images.
This invention relates to medical imaging, specifically techniques for generating parametric images from dynamic imaging data, such as those obtained from positron emission tomography (PET) or other time-resolved imaging modalities. The problem addressed is the need to extract quantitative physiological information from dynamic imaging data, which typically involves complex temporal variations in signal intensity. The method involves processing a sequence of dynamic images to produce parametric images that reflect specific physiological parameters, such as blood flow, metabolic rate, or receptor binding. First, an activity map is generated from the dynamic images, representing spatial distributions of a radiotracer or contrast agent over time. Next, a time-activity curve (TAC) is derived for each pixel or region in the dynamic images, capturing the temporal changes in signal intensity. The parametric image is then generated by combining the activity map with the TAC data, allowing for the calculation of physiological parameters at each spatial location. This approach enhances the diagnostic value of dynamic imaging by providing spatially resolved quantitative metrics rather than raw signal intensities. The method may be applied in clinical settings to improve the assessment of disease states, treatment response, or biological processes.
13. The method of claim 12 , wherein the generating the at least one parametric image further includes using an objective function including a penalty term, the penalty term including one or more spatially-varying smoothing parameters determined from the activity map.
This invention relates to medical imaging, specifically techniques for generating parametric images from dynamic imaging data, such as PET or SPECT scans. The problem addressed is the need for improved accuracy in parametric imaging, particularly in handling spatially varying noise and activity levels in the data. The method involves generating parametric images by fitting a kinetic model to dynamic imaging data, where the fitting process incorporates an objective function with a penalty term. The penalty term includes one or more spatially-varying smoothing parameters that are determined from an activity map derived from the imaging data. The activity map reflects the distribution of radiotracer uptake or other activity within the imaged region. By adjusting the smoothing parameters based on this activity map, the method reduces noise in low-activity regions while preserving detail in high-activity regions, leading to more accurate parametric images. The kinetic model used for fitting may be a compartmental model or another suitable model describing the behavior of the radiotracer or other imaging agent. The dynamic imaging data consists of a series of images acquired over time, capturing the temporal changes in activity. The activity map is generated by processing the dynamic imaging data to highlight regions of interest or varying activity levels. The spatially-varying smoothing parameters are then derived from this map to guide the fitting process, ensuring that the parametric images are both smooth and accurate across different regions of the image. This approach improves the reliability of quantitative analysis in medical imaging applications.
14. The method of claim 13 , wherein the penalty term includes a first parameter controlling the degree of penalty based on a Patlak slope of the time activity curve, and a second parameter controlling the degree of penalty based on a Patlak intercept of the time activity curve.
This invention relates to dynamic positron emission tomography (PET) imaging, specifically methods for improving the accuracy of kinetic modeling by incorporating penalty terms in the analysis of time-activity curves (TACs). The problem addressed is the variability and noise in TAC data, which can lead to unreliable kinetic parameter estimates, such as those derived from Patlak analysis. Patlak analysis is a graphical method used to quantify irreversible tracer uptake, but its accuracy depends on the linearity of the TAC data, which is often compromised by noise or physiological fluctuations. The invention introduces a method that enhances Patlak analysis by applying penalty terms to the TAC data. These penalty terms include a first parameter that adjusts the penalty based on the slope of the Patlak plot, ensuring that deviations from expected linear behavior are appropriately weighted. A second parameter controls the penalty based on the intercept of the Patlak plot, which reflects the initial tracer distribution. By tuning these parameters, the method reduces the impact of noise and improves the reliability of kinetic parameter estimates. The approach is particularly useful in clinical and research settings where precise quantification of tracer uptake is critical, such as in oncology or neurology. The method can be applied to any dynamic PET imaging scenario where Patlak analysis is used, providing more robust and accurate results.
15. The method of claim 13 , further comprising: segmenting the activity map based on types of organs represented therein; and generating the at least one parametric image, wherein the penalty term is based on the segmentation of the activity map.
This invention relates to medical imaging, specifically techniques for generating parametric images from activity maps, such as those derived from positron emission tomography (PET) or single-photon emission computed tomography (SPECT) scans. The problem addressed is the need to improve the accuracy and interpretability of parametric images by incorporating anatomical information from segmented organ regions. The method involves segmenting an activity map into distinct regions corresponding to different types of organs. The segmentation process identifies and delineates anatomical structures within the map, distinguishing between organs such as the heart, liver, or brain. Once segmented, the activity map is used to generate at least one parametric image, which quantifies physiological or biochemical properties (e.g., metabolic rate, blood flow). A penalty term is applied during the generation of the parametric image, where the penalty is based on the segmentation results. This penalty term helps constrain the parametric image to respect anatomical boundaries, reducing artifacts and improving spatial accuracy. By integrating segmentation with parametric image generation, the method enhances the reliability of quantitative imaging, making it more suitable for clinical diagnosis and treatment planning. The approach ensures that the parametric image aligns with known anatomical structures, avoiding misinterpretation due to spurious activity or noise. This technique is particularly useful in applications requiring precise localization of physiological parameters within specific organs.
16. The method of claim 12 , wherein the generating the at least one parametric image further includes using an objective function including a penalty term, wherein the penalty term includes an edge-preserving potential function that has a shape based on the activity map.
This invention relates to medical imaging, specifically techniques for generating parametric images from medical scan data, such as PET or MRI scans. The problem addressed is the need to improve the accuracy and clarity of parametric images, which are used to quantify physiological or biochemical properties in tissues. Traditional methods often produce blurred or distorted images due to noise and artifacts, making it difficult to distinguish fine structures like edges or boundaries between different tissue types. The invention describes a method for generating parametric images with enhanced edge preservation. The method involves creating an activity map from the scan data, which highlights regions of high activity or contrast. This activity map is then used to shape an edge-preserving potential function within an objective function. The potential function penalizes deviations that would blur or distort edges, ensuring that the final parametric image retains sharp boundaries. The objective function optimizes the image reconstruction process, balancing data fidelity with edge preservation to produce a clearer and more accurate representation of the underlying tissue properties. This approach is particularly useful in applications where precise delineation of tissue boundaries is critical, such as tumor detection or functional brain imaging.
17. The method of claim 11 , wherein the generating the at least one parametric image further includes using a voxel-by-voxel parametric fitting for the each of the plurality of dynamic sinograms.
This invention relates to medical imaging, specifically to the generation of parametric images from dynamic sinograms in positron emission tomography (PET) or similar imaging modalities. The problem addressed is the accurate and efficient extraction of quantitative physiological parameters from dynamic imaging data, which is crucial for applications like tumor characterization, metabolic studies, and drug development. The method involves processing dynamic sinograms, which are time-resolved projection data collected during a PET scan. Each dynamic sinogram represents the spatial distribution of radiotracer uptake over time. The invention improves upon prior techniques by incorporating a voxel-by-voxel parametric fitting step. This means that for each individual voxel (a 3D pixel) in the reconstructed image, a mathematical model is applied to the corresponding time-activity data in the sinogram to estimate physiological parameters such as blood flow, metabolic rate, or receptor density. The parametric fitting is performed independently for each voxel, allowing for high spatial resolution in the resulting parametric images. This approach enhances the accuracy of parameter estimation by accounting for local variations in tracer kinetics, which is particularly important in heterogeneous tissues like tumors. The method may also include preprocessing steps such as noise reduction or motion correction to improve the quality of the dynamic sinograms before fitting. The output is a parametric image where each voxel value represents a derived physiological parameter rather than raw tracer concentration, providing more clinically relevant information.
18. The method of claim 17 , wherein the parametric fitting uses a least squares estimation.
A system and method for parametric fitting of data involves analyzing input data to determine optimal parameters for a mathematical model. The method includes receiving input data, selecting a parametric model, and fitting the model to the data using an optimization technique. The optimization process adjusts the model parameters to minimize the difference between the model predictions and the input data. In some implementations, the fitting process uses a least squares estimation, which calculates the sum of squared differences between the model and the data and iteratively refines the parameters to minimize this sum. This approach ensures that the model closely approximates the input data while maintaining computational efficiency. The method may also include validating the fitted model by comparing its predictions to additional test data or by assessing statistical metrics such as residual errors. The system can be applied in various fields, including signal processing, image analysis, and scientific data modeling, where accurate parameter estimation is critical for interpreting complex datasets. The use of least squares estimation provides a robust and widely accepted technique for parameter fitting, ensuring reliable and reproducible results.
19. The method of claim 11 , wherein the activity map is generated using an ordered-subset expectation maximization (OSEM) method, which is iteratively repeated until a predetermined stopping criteria is reached.
This invention relates to medical imaging, specifically methods for generating activity maps in nuclear medicine imaging systems, such as positron emission tomography (PET) or single-photon emission computed tomography (SPECT). The problem addressed is the need for accurate and efficient reconstruction of activity distributions within a patient's body from detected radiation events, which is computationally intensive and prone to noise and artifacts. The method involves generating an activity map by iteratively applying an ordered-subset expectation maximization (OSEM) algorithm. OSEM is a statistical reconstruction technique that processes subsets of projection data in sequential order to improve convergence speed and reduce noise compared to traditional full-data-set methods. The algorithm is repeated in iterations until a predefined stopping criterion is met, such as a maximum number of iterations or a convergence threshold. This ensures the activity map converges to a stable representation of the true activity distribution while balancing computational efficiency and image quality. The method may also incorporate additional steps, such as preprocessing raw detector data, applying corrections for system-specific factors like detector sensitivity or attenuation, and post-processing the reconstructed image to enhance visualization or quantitative analysis. The goal is to produce high-fidelity activity maps that aid in diagnostic interpretation or treatment planning.
20. A non-transitory computer-readable storage medium including executable instructions, which when executed by circuitry, cause the circuitry to perform the method according to claim 11 .
A system and method for optimizing data processing in a distributed computing environment addresses inefficiencies in task scheduling and resource allocation. The invention focuses on improving performance by dynamically adjusting task distribution across multiple computing nodes based on real-time workload analysis. The method involves monitoring computational resources, such as CPU usage, memory availability, and network bandwidth, to identify bottlenecks. It then redistributes tasks to underutilized nodes while prioritizing critical operations to minimize latency. The system also employs predictive algorithms to anticipate future workload demands, allowing proactive resource allocation. Additionally, it includes fault detection mechanisms to reroute tasks from failing nodes to maintain system stability. The invention ensures balanced workload distribution, reduces idle time, and enhances overall system throughput. By dynamically adapting to changing conditions, it optimizes resource utilization and improves processing efficiency in distributed computing environments.
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April 24, 2020
February 15, 2022
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